Dynamic Data-Driven Fault Diagnosis of Wind Turbine Systems

In this multi-university collaborative research, we will develop a framework for the dynamic data-driven fault diagnosis of wind turbines which aims at making the wind energy a competitive alternative in the energy market. This new methodology is fundamentally different from the current practice whose performance is limited due to the non-dynamic and non-robust nature in the modeling approaches and in the data collection and processing strategies. The new methodology consists of robust data pre-processing modules, interrelated, multi-level models that describe different details of the system behaviors, and a dynamic strategy that allows for measurements to be adaptively taken according to specific physical conditions and the associated risk level. This paper summarizes the latest progresses in the research.

• Type: Conference
• Year: 2007
• Where: ICCS
• Authors: Yu Ding, Eunshin Byon, Chiwoo Park, Jiong Tang, Yi Lu, Xin Wang
• Keywords: Data Pre-processing Modules, Dynamic Data-driven Fault, ICCS 2007, Multi-university Collaborative Research